Having a small sample and a large effect doesn't mean you're guaranteed at least some effect on a large sample. If anything, a very large effect is more likely to indicate a flaw in your method.
An example of this is publication bias, which occurs when scientists report results only when the estimated effect sizes are large or are statistically significant. As a result, if many researchers carry out studies with low statistical power, the reported effect sizes will tend to be larger than the true (population) effects, if any.
Like they did in this paper.
0: http://daniellakens.blogspot.com/2017/07/impossibly-hungry-j...
Farmer: huh, we'll I'll be damned. Who in the hell woulda thought to feed them seaweed."
Methane is odorless https://en.wikipedia.org/wiki/Methane
My dog farts the most noxious gas if he eats apple or chicken.